2 papers
math.NA2026
L^1 data fitting for Inverse Problems yields optimal rates of convergence in case of discretized white Gaussian noise
Kristina Bätz, Frank Werner
It is well-known in practice, that L^1 data fitting leads to improved robustness compared to standard L^2 data fitting. However, it is unclear whether resulting algorithms will per…
math.ST2025
Maximum a posteriori testing in statistical inverse problems
Remo Kretschmann, Frank Werner
This paper is concerned with a Bayesian approach to testing hypotheses in statistical inverse problems. Based on the posterior distribution , we want t…